Articles | Volume 15, issue 6
Atmos. Meas. Tech., 15, 1829–1848, 2022
https://doi.org/10.5194/amt-15-1829-2022
Atmos. Meas. Tech., 15, 1829–1848, 2022
https://doi.org/10.5194/amt-15-1829-2022
Research article
25 Mar 2022
Research article | 25 Mar 2022

Identification of tropical cyclones via deep convolutional neural network based on satellite cloud images

Biao Tong et al.

Data sets

An overview of the China Meteorological Administration tropical cyclone database (http://tcdata.typhoon.org.cn/) M. Ying, W. Zhang, H. Yu, X. Lu, J. Feng, Y. Fan, Y. Zhu, and D. Chen https://doi.org/10.1175/JTECH-D-12-00119.1

Western north pacific tropical cyclone database created by the China meteorological administration (http://tcdata.typhoon.org.cn/) X. Lu, H. Yu, M. Ying, B. Zhao, S. Zhang, L. Lin, L. Bai, and R. Wan https://doi.org/10.1007/s00376-020-0211-7

Download
Short summary
In recent years, there has been numerous research on tropical cyclone (TC) observation based on satellite cloud images (SCIs), but most methods are limited by low efficiency and subjectivity. To overcome subjectivity and improve efficiency of traditional methods, this paper uses deep learning technology to do further research on fingerprint identification of TCs. Results provide an automatic and objective method to distinguish TCs from SCIs and are convenient for subsequent research.